Climate Change and Public Health

University of Birmingham
n.dasandi@bham.ac.uk

Hertie School
jankin@hertie-school.org

Abstract

We look at the link between public health issues and climate change discussed in the UN General Debates.

#Data

Loading data from the UNGDC data

#Loading packages and data
library(readtext)
library(quanteda)
library(dplyr)
library(tidyr)
library(stringr)
library(ggplot2)
library(rworldmap)
library(RColorBrewer)
library(haven)
library(readxl)

DATA_DIR <- "~/Dropbox/Research/UNGDC projects/UN Data/" 

ungd_files <- readtext(paste0(DATA_DIR, "TXT/*"), 
                                 docvarsfrom = "filenames", 
                                 dvsep="_", 
                                 docvarnames = c("Country", "Session", "Year"))


ungd_files$doc_id <- str_replace(ungd_files$doc_id , ".txt", "") %>%
   str_replace(. , "_\\d{2}", "")

##Creating corpus object(s)

ungd_corpus <- corpus(ungd_files, text_field = "text") 

ungdc.2019 <- corpus_subset(ungd_corpus, Year==2019)

corp_summary <- summarise(group_by(summary(ungd_corpus, n = 9349),Year),
                           total_speeches=n(),total_sentences=sum(Sentences),total_words=sum(Tokens))

readr::write_csv(corp_summary, "corp_summary.csv")

#Pre-processing

Tokenizing corpus.

#Tokenization and basic pre-processing
tok <- tokens(ungd_corpus, what = "word",
              remove_punct = TRUE,
              remove_symbols = TRUE,
              remove_numbers = TRUE,
              remove_url = TRUE,
              split_hyphens = FALSE,
              verbose = TRUE)

Creating a tokens object from a corpus input...
...starting tokenization
...preserving hyphens
...preserving social media tags (#, @)
...tokenizing 1 of 1 blocks
...segmenting tokens
...serializing tokens 103147 unique types
...removing separators, punctuation, symbols, numbers, URLs 
...total elapsed:  23 seconds.
Finished constructing tokens from 9,349 texts.

Lowercasing and removing stopwords

tok <- tokens_tolower(tok)
tok.r <- tokens_select(tok, stopwords("english"), selection = "remove", padding = FALSE)

#Setting up agreed dictionaries

Creating compound tokens from the key terms (phrases) in our dictionaries:

mylist <- list( c("air", "pollution"), c("mental", "disorder"), c("mental", "disorders"), c("climate","change"), c("changing","climate"), c("climate","emergency"), c("climate","crisis"), c("climate","decay"), c("global","warming"), c("green","house"), c("extreme","weather"), c("global", "environmental", "change"), c("climate","variability"),  c("low","carbon"), c("renewable","energy"), c("carbon","emission"), c("carbon","emissions"), c("carbon","dioxide"), c("co2","emission"), c("co2","emissions"), c("climate","pollutant"), c("climate","pollutants"), c("carbon","neutral"), c("carbon","neutrality"), c("climate","neutrality"), c("climate","action"), c("net","zero")) 
 

tok.compound <- tokens_compound(tok.r, mylist, valuetype = "fixed", concatenator = "_")

Creating the dictionary of climate change terms:

climate_dict <- dictionary(list(climate =  c("climate_change", "changing_climate", "climate_emergency", "climate_crisis", "climate_decay", "global_warming", "green_house", "temperature", "extreme_weather", "global_environmental_change", "climate_variability", "greenhouse", "greenhouse-gas", "low_carbon", "ghge", "ghges", "renewable_energy", "carbon_emission", "carbon_emissions", "carbon_dioxide", "carbon-dioxide", "co2_emission", "co2_emissions", "climate_pollutant", "climate_pollutants", "decarbonization", "decarbonisation", "carbon_neutral", "carbon-neutral", "carbon_neutrality", "climate_neutrality", "climate_action", "net-zero", "net_zero"
)))

Creating the dictionary of health terms:

health_dict <- dictionary(list(health = c("malaria", "diarrhoea", "infection", "disease", "diseases", "sars", "measles", "pneumonia", "epidemic", "epidemics", "pandemic", "pandemics", "epidemiology", "healthcare", "health", "mortality", "morbidity", "nutrition", "illness", "illnesses", "ncd", "ncds", "air_pollution", "nutrition", "malnutrition", "malnourishment", "mental_disorder", "mental_disorders", "stunting")))

KWIC

Performing a keyword-in-context search. The window is set to 25 words before and after the term - reflecting approximately half a paragraph before and after the term (on average a paragraph in English is 50 words). UNGD speeches are highly structured documents drafted over long periods of time and reflect internal discussions and negotiations in government. Hence we assume that terms appearing within specific proximity of each other are related through the structured process of document generation.

We search for terms from our health dictionary and show the 25-word context around them.

tok.hea <- kwic(tok.compound, health_dict, window = 25, valuetype = "fixed")

readr::write_csv(tok.hea, "health_kwic_25_fixed.csv")

We search for terms from our climate change dictionary and show the 25-word context around them.

tok.cc <- kwic(tok.compound, climate_dict, window = 25, valuetype = "fixed")

readr::write_csv(tok.cc, "climate_kwic_25_fixed.csv")

To tap into the core proposition of this analysis - the link between climate change and health terms - we take the 25-word context around the health terms and search this context for climate change terms.

Text surrounding health terms (25 words each side) is saved and transformed to a corpus object. We then perform key-word-in-context search of this health related corpus for the terms in our climate change dictionary.

corpus_health <- corpus(tok.hea, split_context = FALSE, extract_keyword = TRUE)

tok.climate.kwic <- kwic(corpus_health, climate_dict, window = 25, valuetype = "fixed")

readr::write_csv(tok.climate.kwic, "intersection_kwic_25_fixed.csv")

Robustness analysis

For robustness analysis we looked at different context window sizes and manually assessed them for the balance of false positives vs false negatives.

tok.hea.5 <- kwic(tok.compound, health_dict, window = 5, valuetype = "fixed")
tok.cc.5 <- kwic(tok.compound, climate_dict, window = 5, valuetype = "fixed")
corpus_health.5 <- corpus(tok.hea.5, split_context = FALSE, extract_keyword = TRUE)
tok.climate.kwic.5 <- kwic(corpus_health.5, climate_dict, window = 5, valuetype = "fixed")
readr::write_csv(tok.climate.kwic.5, "intersection_kwic_5_fixed.csv")
readr::write_csv(tok.hea.5, "health_kwic_5_fixed.csv")
readr::write_csv(tok.cc.5, "climate_kwic_5_fixed.csv")

tok.hea.10 <- kwic(tok.compound, health_dict, window = 10, valuetype = "fixed")
tok.cc.10 <- kwic(tok.compound, climate_dict, window = 10, valuetype = "fixed")
corpus_health.10 <- corpus(tok.hea.10, split_context = FALSE, extract_keyword = TRUE)
tok.climate.kwic.10 <- kwic(corpus_health.10, climate_dict, window = 10, valuetype = "fixed")
readr::write_csv(tok.climate.kwic.10, "intersection_kwic_10_fixed.csv")
readr::write_csv(tok.hea.10, "health_kwic_10_fixed.csv")
readr::write_csv(tok.cc.10, "climate_kwic_10_fixed.csv")

tok.hea.30 <- kwic(tok.compound, health_dict, window = 30, valuetype = "fixed")
tok.cc.30 <- kwic(tok.compound, climate_dict, window = 30, valuetype = "fixed")
corpus_health.30 <- corpus(tok.hea.30, split_context = FALSE, extract_keyword = TRUE)
tok.climate.kwic.30 <- kwic(corpus_health.30, climate_dict, window = 30, valuetype = "fixed")
readr::write_csv(tok.climate.kwic.30, "intersection_kwic_30_fixed.csv")
readr::write_csv(tok.hea.30, "health_kwic_30_fixed.csv")
readr::write_csv(tok.cc.30, "climate_kwic_30_fixed.csv")

tok.hea.50 <- kwic(tok.compound, health_dict, window = 50, valuetype = "fixed")
tok.cc.50 <- kwic(tok.compound, climate_dict, window = 50, valuetype = "fixed")
corpus_health.50 <- corpus(tok.hea.50, split_context = FALSE, extract_keyword = TRUE)
tok.climate.kwic.50 <- kwic(corpus_health.50, climate_dict, window = 50, valuetype = "fixed")
readr::write_csv(tok.climate.kwic.50, "intersection_kwic_50_fixed.csv")
readr::write_csv(tok.hea.50, "health_kwic_50_fixed.csv")
readr::write_csv(tok.cc.50, "climate_kwic_50_fixed.csv")

health_dfm <- dfm(corpus_health)

corpus_climate <- corpus(tok.cc, split_context = FALSE, extract_keyword = TRUE)
climate_dfm <- dfm(corpus_climate)

corpus_intersection <- corpus(tok.climate.kwic, split_context = FALSE, extract_keyword = TRUE)
intersection_dfm <- dfm(corpus_intersection)

health <- convert(health_dfm, "data.frame")
climate <- convert(climate_dfm, "data.frame")
intersection <- convert(intersection_dfm, "data.frame")

names(health)[1] <- "docid"
names(climate)[1] <- "docid"
names(intersection)[1] <- "docid"

health$"docid" <- str_extract(health$docid, "^([^.]+)")
climate$"docid" <- str_extract(climate$docid, "^([^.]+)")
intersection$"docid" <- str_extract(intersection$docid, "^([^.]+)")

health_counts <- health %>% group_by(docid) %>% summarise(health_count = n())
climate_counts <- climate %>% group_by(docid) %>% summarise(climate_count = n())
intersection_counts <- intersection %>% group_by(docid) %>% summarise(intersection_count = n())

total_counts <- climate_counts %>% 
  full_join(health_counts, by = "docid") %>% 
  full_join(intersection_counts, by = "docid") %>%
  replace_na(list(health_count=0, climate_count=0, intersection_count=0)) %>%
  separate(docid, c("country", "year"), "_")

total_counts$year <- as.numeric(total_counts$year)

total_counts <- arrange(total_counts, country, year)

readr::write_csv(total_counts, "total_counts.csv")

Maps of mention counts

Map for 2019 of PH and CC

Keeping only country-years with at least one mention of CC in PH conext

map <- joinCountryData2Map(subset(total_counts, year==2019 & health_count>0), joinCode="ISO3", nameJoinColumn="country")

117 codes from your data successfully matched countries in the map
0 codes from your data failed to match with a country code in the map
126 codes from the map weren't represented in your data

new_world <- subset(map, continent != "Antarctica")

pdf("worldmap_2019_health.pdf", width = 7, height = 3)

par(mai=c(0,0,0.2,0),xaxs="i",yaxs="i")

mapParams <- mapCountryData(new_world, nameColumnToPlot="health_count", 
                            mapTitle="2019 UN General Debate: Health", 
                            catMethod = "categorical", 
                            colourPalette = "heat", 
                            oceanCol = "lightblue", 
                            missingCountryCol = "white", 
                            addLegend="FALSE")

do.call( addMapLegendBoxes, c(mapParams,title="Number of mentions",x = "bottom", horiz=TRUE, cex=0.5))

dev.off()

null device 
          1 

map <- joinCountryData2Map(subset(total_counts, year==2019 & climate_count>0), joinCode="ISO3", nameJoinColumn="country")

174 codes from your data successfully matched countries in the map
1 codes from your data failed to match with a country code in the map
69 codes from the map weren't represented in your data

new_world <- subset(map, continent != "Antarctica")

pdf("worldmap_2019_climate.pdf", width = 7, height = 3)

par(mai=c(0,0,0.2,0),xaxs="i",yaxs="i")

mapParams <- mapCountryData(new_world, nameColumnToPlot="climate_count", 
                            mapTitle="2019 UN General Debate: Climate Change", 
                            catMethod = "categorical", 
                            colourPalette = "heat", 
                            oceanCol = "lightblue", 
                            missingCountryCol = "white", 
                            addLegend="FALSE")

do.call( addMapLegendBoxes, c(mapParams,title="Number of mentions",x = "bottom", horiz=TRUE, cex=0.5))

dev.off()

null device 
          1 

map <- joinCountryData2Map(subset(total_counts, year==2019 & intersection_count>0), joinCode="ISO3", nameJoinColumn="country")

43 codes from your data successfully matched countries in the map
0 codes from your data failed to match with a country code in the map
200 codes from the map weren't represented in your data

new_world <- subset(map, continent != "Antarctica")

pdf("worldmap_2019_intersection.pdf", width = 7, height = 3)

par(mai=c(0,0,0.2,0),xaxs="i",yaxs="i")

mapParams <- mapCountryData(new_world, nameColumnToPlot="intersection_count", 
                            mapTitle="2019 UN General Debate: Intersection", 
                            catMethod = "categorical", 
                            colourPalette = "heat", 
                            oceanCol = "lightblue", 
                            missingCountryCol = "white", 
                            addLegend="FALSE")

do.call( addMapLegendBoxes, c(mapParams,title="Number of mentions",x = "bottom", horiz=TRUE, cex=0.5))

dev.off()

null device 
          1 

Country plots

##USA

total_counts <- total_counts %>% add_row(country = "USA", year = 2018, climate_count = 0, health_count = 0, intersection_count  =  0)
total_counts <- total_counts %>% add_row(country = "USA", year = 2019, climate_count = 0, health_count = 0, intersection_count  =  0)

ggplot(subset(total_counts, country=="USA"), aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= health_count), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= climate_count), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
  geom_line(aes(y= intersection_count), colour = "red", alpha = 0.9) +
  ggtitle("USA") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

ggsave("timeseries_all_total_count_USA.pdf")

Saving 7.29 x 4.51 in image

##GBR

total_counts <- total_counts %>% add_row(country = "GBR", year = 1970, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "GBR", year = 1971, climate_count = 0, health_count = 0, intersection_count  =  0)

ggplot(subset(total_counts, country=="GBR"), aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= health_count), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= climate_count), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
  geom_line(aes(y= intersection_count), colour = "red", alpha = 0.9) +
  ggtitle("UK") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

ggsave("timeseries_all_total_count_GBR.pdf")

Saving 7.29 x 4.51 in image

##Germany

total_counts <- total_counts %>% add_row(country = "DEU", year = 1970, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "DEU", year = 1971, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "DEU", year = 1972, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "DEU", year = 1973, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "DEU", year = 1974, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "DEU", year = 1975, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "DEU", year = 1976, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "DEU", year = 1977, climate_count = 0, health_count = 0, intersection_count  =  0)


ggplot(subset(total_counts, country=="DEU"), aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= health_count), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= climate_count), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
  geom_line(aes(y= intersection_count), colour = "red", alpha = 0.9) +
  ggtitle("Germany") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

ggsave("timeseries_all_total_count_DEU.pdf")

Saving 7.29 x 4.51 in image

##France

ggplot(subset(total_counts, country=="FRA"), aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= health_count), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= climate_count), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
  geom_line(aes(y= intersection_count), colour = "red", alpha = 0.9) +
  ggtitle("France") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

ggsave("timeseries_all_total_count_FRA.pdf")

Saving 7.29 x 4.51 in image

##China

total_counts <- total_counts %>% add_row(country = "CHN", year = 1971, climate_count = 0, health_count = 0, intersection_count  =  0) 

ggplot(subset(total_counts, country=="CHN"), aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= health_count), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= climate_count), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
  geom_line(aes(y= intersection_count), colour = "red", alpha = 0.9) +
  ggtitle("China") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

ggsave("timeseries_all_total_count_CHN.pdf")

Saving 7.29 x 4.51 in image

##Norway

total_counts <- total_counts %>% add_row(country = "NOR", year = 1970, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "NOR", year = 1971, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "NOR", year = 1972, climate_count = 0, health_count = 0, intersection_count  =  0)

ggplot(subset(total_counts, country=="NOR"), aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= health_count), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= climate_count), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
  geom_line(aes(y= intersection_count), colour = "red", alpha = 0.9) +
  ggtitle("Norway") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

ggsave("timeseries_all_total_count_NOR.pdf")

Saving 7.29 x 4.51 in image

##Australia

total_counts <- total_counts %>% add_row(country = "AUS", year = 1970, climate_count = 0, health_count = 0, intersection_count  =  0)

ggplot(subset(total_counts, country=="AUS"), aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= health_count), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= climate_count), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
  geom_line(aes(y= intersection_count), colour = "red", alpha = 0.9) +
  ggtitle("Australia") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

ggsave("timeseries_all_total_count_AUS.pdf")

Saving 7.29 x 4.51 in image

##India

total_counts <- total_counts %>% add_row(country = "IND", year = 1970, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "IND", year = 1971, climate_count = 0, health_count = 0, intersection_count  =  0)

ggplot(subset(total_counts, country=="IND"), aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= health_count), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= climate_count), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
  geom_line(aes(y= intersection_count), colour = "red", alpha = 0.9) +
  ggtitle("India") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

ggsave("timeseries_all_total_count_IND.pdf")

Saving 7.29 x 4.51 in image

##Belgium

total_counts <- total_counts %>% add_row(country = "BEL", year = 1970, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "BEL", year = 1971, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "BEL", year = 1972, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "BEL", year = 1973, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "BEL", year = 1974, climate_count = 0, health_count = 0, intersection_count  =  0) 

ggplot(subset(total_counts, country=="BEL"), aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= health_count), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= climate_count), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
  geom_line(aes(y= intersection_count), colour = "red", alpha = 0.9) +
  ggtitle("Belgium") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

ggsave("timeseries_all_total_count_BEL.pdf")

Saving 7.29 x 4.51 in image

##Japan

total_counts <- total_counts %>% add_row(country = "JPN", year = 1970, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "JPN", year = 1971, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "JPN", year = 1972, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "JPN", year = 1973, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "JPN", year = 1974, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "JPN", year = 1975, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "JPN", year = 1976, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "JPN", year = 1977, climate_count = 0, health_count = 0, intersection_count  =  0) 

ggplot(subset(total_counts, country=="JPN"), aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= health_count), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= climate_count), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
  geom_line(aes(y= intersection_count), colour = "red", alpha = 0.9) +
  ggtitle("Japan") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

ggsave("timeseries_all_total_count_JPN.pdf")

Saving 7.29 x 4.51 in image

#EU

#eu presidencies

total_counts$country_alt <- total_counts$country

total_counts$country_alt[total_counts$country== "BEL" & total_counts$year == 1970] <- "EU"
total_counts$country_alt[total_counts$country== "ITA" & total_counts$year == 1971] <- "EU"
total_counts$country_alt[total_counts$country== "NLD" & total_counts$year == 1972] <- "EU"
total_counts$country_alt[total_counts$country== "DNK" & total_counts$year == 1973] <- "EU"
total_counts$country_alt[total_counts$country== "FRA" & total_counts$year == 1974] <- "EU"
total_counts$country_alt[total_counts$country== "ITA" & total_counts$year == 1975] <- "EU"
total_counts$country_alt[total_counts$country== "NLD" & total_counts$year == 1976] <- "EU"
total_counts$country_alt[total_counts$country== "BEL" & total_counts$year == 1977] <- "EU"
total_counts$country_alt[total_counts$country== "DEU" & total_counts$year == 1978] <- "EU"
total_counts$country_alt[total_counts$country== "IRL" & total_counts$year == 1979] <- "EU"
total_counts$country_alt[total_counts$country== "LUX" & total_counts$year == 1980] <- "EU"
total_counts$country_alt[total_counts$country== "GBR" & total_counts$year == 1981] <- "EU"
total_counts$country_alt[total_counts$country== "DNK" & total_counts$year == 1982] <- "EU"
total_counts$country_alt[total_counts$country== "GRC" & total_counts$year == 1983] <- "EU"
total_counts$country_alt[total_counts$country== "IRL" & total_counts$year == 1984] <- "EU"
total_counts$country_alt[total_counts$country== "LUX" & total_counts$year == 1985] <- "EU"
total_counts$country_alt[total_counts$country== "GBR" & total_counts$year == 1986] <- "EU"
total_counts$country_alt[total_counts$country== "DNK" & total_counts$year == 1987] <- "EU"
total_counts$country_alt[total_counts$country== "GRC" & total_counts$year == 1988] <- "EU"
total_counts$country_alt[total_counts$country== "FRA" & total_counts$year == 1989] <- "EU"
total_counts$country_alt[total_counts$country== "ITA" & total_counts$year == 1990] <- "EU"
total_counts$country_alt[total_counts$country== "NLD" & total_counts$year == 1991] <- "EU"
total_counts$country_alt[total_counts$country== "GBR" & total_counts$year == 1992] <- "EU"
total_counts$country_alt[total_counts$country== "BEL" & total_counts$year == 1993] <- "EU"
total_counts$country_alt[total_counts$country== "DEU" & total_counts$year == 1994] <- "EU"
total_counts$country_alt[total_counts$country== "ESP" & total_counts$year == 1995] <- "EU"
total_counts$country_alt[total_counts$country== "IRL" & total_counts$year == 1996] <- "EU"
total_counts$country_alt[total_counts$country== "LUX" & total_counts$year == 1997] <- "EU"
total_counts$country_alt[total_counts$country== "AUT" & total_counts$year == 1998] <- "EU"
total_counts$country_alt[total_counts$country== "FIN" & total_counts$year == 1999] <- "EU"
total_counts$country_alt[total_counts$country== "FRA" & total_counts$year == 2000] <- "EU"
total_counts$country_alt[total_counts$country== "BEL" & total_counts$year == 2001] <- "EU"
total_counts$country_alt[total_counts$country== "DNK" & total_counts$year == 2002] <- "EU"
total_counts$country_alt[total_counts$country== "ITA" & total_counts$year == 2003] <- "EU"
total_counts$country_alt[total_counts$country== "NLD" & total_counts$year == 2004] <- "EU"
total_counts$country_alt[total_counts$country== "GBR" & total_counts$year == 2005] <- "EU"
total_counts$country_alt[total_counts$country== "FIN" & total_counts$year == 2006] <- "EU"
total_counts$country_alt[total_counts$country== "PRT" & total_counts$year == 2007] <- "EU"
total_counts$country_alt[total_counts$country== "FRA" & total_counts$year == 2008] <- "EU"
total_counts$country_alt[total_counts$country== "SWE" & total_counts$year == 2009] <- "EU"
total_counts$country_alt[total_counts$country== "BEL" & total_counts$year == 2010] <- "EU"

total_counts <- total_counts %>% add_row(country_alt = "EU", year = 1970, climate_count = 0, health_count = 0, intersection_count  =  0) %>%  add_row(country_alt = "EU", year = 1971, climate_count = 0, health_count = 0, intersection_count  =  0)

ggplot(subset(total_counts, country_alt=="EU"), aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= health_count), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= climate_count), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
  geom_line(aes(y= intersection_count), colour = "red", alpha = 0.9) +
  ggtitle("EU") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

ggsave("timeseries_all_total_count_EU.pdf")

Saving 7.29 x 4.51 in image

Time series of total counts plot

total counts

# calculating the total number of mentions by year
sum <- summarise(group_by(total_counts, year), 
                 sum_CC = sum(climate_count), sum_PH = sum(health_count), sum_int = sum(intersection_count),
                 mean_CC = mean(climate_count), mean_PH = mean(health_count), mean_int = mean(intersection_count))

ggplot(sum, aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= sum_PH), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= sum_CC), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
#  geom_line(aes(y= count), colour = "black", alpha = 0.9) +
  #ggtitle("Political engagement with the intersection of climate change and health") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))+
 annotate("text", x = 2000, y = 850, label = "Climate Change", colour = "darkgreen")+
  annotate("text", x = 1993, y = 250, label = "Health", colour = "blue")

ggsave("timeseries_PHCC_total_count.pdf")

Saving 7.29 x 4.51 in image

ggplot(sum, aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= sum_int), colour = "red", alpha = 0.9) +
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019)) +
   annotate("text", x = 2015, y = 20, label = "Intersection", colour = "red")

ggsave("timeseries_intersection_total_count.pdf")

Saving 7.29 x 4.51 in image

readr::write_csv(sum, "figure57.csv")

ggplot(sum, aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= sum_PH), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= sum_CC), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
  geom_line(aes(y= sum_int), colour = "red", alpha = 0.9) +
  #ggtitle("Political engagement with the intersection of climate change and health") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))+
 annotate("text", x = 2000, y = 850, label = "Climate Change", colour = "darkgreen")+
  annotate("text", x = 1993, y = 250, label = "Health", colour = "blue") +
  annotate("text", x = 2015, y = 100, label = "Intersection", colour = "red")

ggsave("timeseries_all_total_count.pdf")

Saving 7.29 x 4.51 in image

Proportion of countries presentation

Proportion of countries that had at least one mention

Data preparation

climate_speeches <- total_counts %>% filter(climate_count>0) %>% group_by(year) %>% tally(name = "climate_speeches")
health_speeches <- total_counts %>% filter(health_count>0) %>% group_by(year) %>% tally(name = "health_speeches")
intersection_speeches <- total_counts %>% filter(intersection_count>0) %>% group_by(year) %>% tally(name = "intersection_speeches")
total_speeches <- ungd_files %>% group_by(Year) %>% tally(name = "total_speeches")

proportions <- left_join(total_speeches, health_speeches, by = c("Year"="year")) %>% 
  left_join(., climate_speeches, by = c("Year"="year")) %>% 
  left_join(., intersection_speeches, by = c("Year"="year")) %>% 
  replace_na(list(health_speeches=0, climate_speeches=0, intersection_speeches=0))

proportions$percent_int <- proportions$intersection_speeches/proportions$total_speeches*100
proportions$percent_CC <- proportions$climate_speeches/proportions$total_speeches*100
proportions$percent_PH <- proportions$health_speeches/proportions$total_speeches*100

readr::write_csv(proportions, "proportion_results.csv")

Plotting the country proportion

ggplot(proportions, aes(x=Year, y=percent_CC, group = 1)) +
  geom_line(colour="darkgreen", size=1) +
  theme_bw() +
  #ggtitle("Political engagement with the intersection of climate change and health") + 
  ylab("Proportion of countries discussing climate change, %") + xlab("Year") + 
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

 ggsave("total_percent_CC.pdf")

Saving 7.29 x 4.51 in image

ggplot(proportions, aes(x=Year, y=percent_PH, group = 1)) +
  geom_line(colour="blue", size=1) +
  theme_bw() +
  #ggtitle("Political engagement with the intersection of climate change and health") + 
  ylab("Proportion of countries discussing health, %") + xlab("Year") + 
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000,2010, 2019))

  ggsave("total_percent_PH.pdf")

Saving 7.29 x 4.51 in image

ggplot(proportions, aes(x=Year, y=percent_int, group = 1)) +
  geom_line(colour="red", size=1) +
  theme_bw() +
  #ggtitle("Political engagement with the intersection of climate change and health") + 
  ylab("Proportion of countries discussing intersection, %") + xlab("Year") + 
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

  ggsave("total_percent_intersection.pdf")

Saving 7.29 x 4.51 in image

ggplot(proportions, aes(x=Year)) +
  geom_line(aes(y=percent_int), colour="red", size=1) +
  geom_line(aes(y=percent_CC), colour="darkgreen", linetype = "dashed", size=1) +
  geom_line(aes(y=percent_PH), colour="blue", linetype = "dotdash", size=1) +
  theme_bw() +
  #ggtitle("Political engagement with the intersection of climate change and health") + 
  ylab("Proportion of countries, %") + xlab("Year") + 
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))+
 annotate("text", x = 2014, y = 30, label = "Intersection", colour = "red")+
  annotate("text", x = 1975, y = 55, label = "Health", colour = "blue") +
  annotate("text", x = 1988, y = 27, label = "Climate Change", colour = "darkgreen")

  ggsave("all_three_percent.pdf")

Saving 7.29 x 4.51 in image

#Tier Analysis

sids_countries <- c("ASM","AIA","ATG","ABW","BHS","BHR","BRB","BLZ","BMU","CPV","COM","COK","CUB","DMA","DOM","FJI","GRD","GUM","GNB","GUY","HTI","JAM","KIR","MDV","MHL","MUS","FSM","MSR","NRU","NCL","NIU","PLW","PNG","PRI","KNA","LCA","VCT","WSM","STP","SYC","SGP","SLB","SUR","TLS","TON","TTO","TUV","VUT")
tier1_countries <- c("USA","EU", "CHN")
tier2_countries <- c("POL","AUS", "ZAF", "BRA", "IND", "FRA", "DEU", "IDN", "GBR")

total_counts$country_groups <- ifelse(total_counts$country %in% sids_countries, "SIDS", "none")
total_counts$country_groups[total_counts$country %in% tier1_countries] <- "Tier1"
total_counts$country_groups[total_counts$country %in% tier2_countries] <- "Tier2"

#eu presidencies
total_counts$country_groups[total_counts$country== "BEL" & total_counts$year == 1970] <- "Tier1"
total_counts$country_groups[total_counts$country== "ITA" & total_counts$year == 1971] <- "Tier1"
total_counts$country_groups[total_counts$country== "NLD" & total_counts$year == 1972] <- "Tier1"
total_counts$country_groups[total_counts$country== "DNK" & total_counts$year == 1973] <- "Tier1"
total_counts$country_groups[total_counts$country== "FRA" & total_counts$year == 1974] <- "Tier1"
total_counts$country_groups[total_counts$country== "ITA" & total_counts$year == 1975] <- "Tier1"
total_counts$country_groups[total_counts$country== "NLD" & total_counts$year == 1976] <- "Tier1"
total_counts$country_groups[total_counts$country== "BEL" & total_counts$year == 1977] <- "Tier1"
total_counts$country_groups[total_counts$country== "DEU" & total_counts$year == 1978] <- "Tier1"
total_counts$country_groups[total_counts$country== "IRL" & total_counts$year == 1979] <- "Tier1"
total_counts$country_groups[total_counts$country== "LUX" & total_counts$year == 1980] <- "Tier1"
total_counts$country_groups[total_counts$country== "GBR" & total_counts$year == 1981] <- "Tier1"
total_counts$country_groups[total_counts$country== "DNK" & total_counts$year == 1982] <- "Tier1"
total_counts$country_groups[total_counts$country== "GRC" & total_counts$year == 1983] <- "Tier1"
total_counts$country_groups[total_counts$country== "IRL" & total_counts$year == 1984] <- "Tier1"
total_counts$country_groups[total_counts$country== "LUX" & total_counts$year == 1985] <- "Tier1"
total_counts$country_groups[total_counts$country== "GBR" & total_counts$year == 1986] <- "Tier1"
total_counts$country_groups[total_counts$country== "DNK" & total_counts$year == 1987] <- "Tier1"
total_counts$country_groups[total_counts$country== "GRC" & total_counts$year == 1988] <- "Tier1"
total_counts$country_groups[total_counts$country== "FRA" & total_counts$year == 1989] <- "Tier1"
total_counts$country_groups[total_counts$country== "ITA" & total_counts$year == 1990] <- "Tier1"
total_counts$country_groups[total_counts$country== "NLD" & total_counts$year == 1991] <- "Tier1"
total_counts$country_groups[total_counts$country== "GBR" & total_counts$year == 1992] <- "Tier1"
total_counts$country_groups[total_counts$country== "BEL" & total_counts$year == 1993] <- "Tier1"
total_counts$country_groups[total_counts$country== "DEU" & total_counts$year == 1994] <- "Tier1"
total_counts$country_groups[total_counts$country== "ESP" & total_counts$year == 1995] <- "Tier1"
total_counts$country_groups[total_counts$country== "IRL" & total_counts$year == 1996] <- "Tier1"
total_counts$country_groups[total_counts$country== "LUX" & total_counts$year == 1997] <- "Tier1"
total_counts$country_groups[total_counts$country== "AUT" & total_counts$year == 1998] <- "Tier1"
total_counts$country_groups[total_counts$country== "FIN" & total_counts$year == 1999] <- "Tier1"
total_counts$country_groups[total_counts$country== "FRA" & total_counts$year == 2000] <- "Tier1"
total_counts$country_groups[total_counts$country== "BEL" & total_counts$year == 2001] <- "Tier1"
total_counts$country_groups[total_counts$country== "DNK" & total_counts$year == 2002] <- "Tier1"
total_counts$country_groups[total_counts$country== "ITA" & total_counts$year == 2003] <- "Tier1"
total_counts$country_groups[total_counts$country== "NLD" & total_counts$year == 2004] <- "Tier1"
total_counts$country_groups[total_counts$country== "GBR" & total_counts$year == 2005] <- "Tier1"
total_counts$country_groups[total_counts$country== "FIN" & total_counts$year == 2006] <- "Tier1"
total_counts$country_groups[total_counts$country== "PRT" & total_counts$year == 2007] <- "Tier1"
total_counts$country_groups[total_counts$country== "FRA" & total_counts$year == 2008] <- "Tier1"
total_counts$country_groups[total_counts$country== "SWE" & total_counts$year == 2009] <- "Tier1"
total_counts$country_groups[total_counts$country== "BEL" & total_counts$year == 2010] <- "Tier1"


ungd_files$country_groups <- ifelse(ungd_files$Country %in% sids_countries, "SIDS", "none")
ungd_files$country_groups[ungd_files$Country %in% tier1_countries] <- "Tier1"
ungd_files$country_groups[ungd_files$Country %in% tier2_countries] <- "Tier2"


ungd_files$country_groups[ungd_files$Country== "BEL" & ungd_files$Year == 1970] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "ITA" & ungd_files$Year == 1971] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "NLD" & ungd_files$Year == 1972] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "DNK" & ungd_files$Year == 1973] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "FRA" & ungd_files$Year == 1974] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "ITA" & ungd_files$Year == 1975] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "NLD" & ungd_files$Year == 1976] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "BEL" & ungd_files$Year == 1977] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "DEU" & ungd_files$Year == 1978] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "IRL" & ungd_files$Year == 1979] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "LUX" & ungd_files$Year == 1980] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "GBR" & ungd_files$Year == 1981] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "DNK" & ungd_files$Year == 1982] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "GRC" & ungd_files$Year == 1983] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "IRL" & ungd_files$Year == 1984] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "LUX" & ungd_files$Year == 1985] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "GBR" & ungd_files$Year == 1986] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "DNK" & ungd_files$Year == 1987] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "GRC" & ungd_files$Year == 1988] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "FRA" & ungd_files$Year == 1989] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "ITA" & ungd_files$Year == 1990] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "NLD" & ungd_files$Year == 1991] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "GBR" & ungd_files$Year == 1992] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "BEL" & ungd_files$Year == 1993] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "DEU" & ungd_files$Year == 1994] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "ESP" & ungd_files$Year == 1995] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "IRL" & ungd_files$Year == 1996] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "LUX" & ungd_files$Year == 1997] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "AUT" & ungd_files$Year == 1998] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "FIN" & ungd_files$Year == 1999] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "FRA" & ungd_files$Year == 2000] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "BEL" & ungd_files$Year == 2001] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "DNK" & ungd_files$Year == 2002] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "ITA" & ungd_files$Year == 2003] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "NLD" & ungd_files$Year == 2004] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "GBR" & ungd_files$Year == 2005] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "FIN" & ungd_files$Year == 2006] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "PRT" & ungd_files$Year == 2007] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "FRA" & ungd_files$Year == 2008] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "SWE" & ungd_files$Year == 2009] <- "Tier1"
ungd_files$country_groups[ungd_files$Country== "BEL" & ungd_files$Year == 2010] <- "Tier1"

climate_speeches_groups <- total_counts %>% filter(climate_count>0) %>% group_by(year, country_groups) %>% tally(name = "climate_speeches")

health_speeches_groups <- total_counts %>% filter(health_count>0) %>% group_by(year, country_groups) %>% tally(name = "health_speeches")

intersection_speeches_groups <- total_counts %>% filter(intersection_count>0) %>% group_by(year, country_groups) %>% tally(name = "intersection_speeches")


total_speeches_groups <- ungd_files %>% 
  group_by(Year, country_groups) %>% tally(name = "total_speeches")

proportions_groups <- left_join(total_speeches_groups, health_speeches_groups, by = c("Year"="year", "country_groups")) %>% 
  left_join(., climate_speeches_groups, by = c("Year"="year", "country_groups")) %>% 
  left_join(., intersection_speeches_groups, by = c("Year"="year", "country_groups")) %>% 
  replace_na(list(health_speeches=0, climate_speeches=0, intersection_speeches=0))

proportions_groups$percent_int <- proportions_groups$intersection_speeches/proportions_groups$total_speeches*100

proportions_groups$percent_CC <- proportions_groups$climate_speeches/proportions_groups$total_speeches*100

proportions_groups$percent_PH <- proportions_groups$health_speeches/proportions_groups$total_speeches*100

proportions_groups <- proportions_groups %>% filter(country_groups!="none")

##Total count plots by country group

ggplot(proportions_groups, aes(x=Year, y=health_speeches, group=country_groups, linetype = country_groups, colour = country_groups)) +
    geom_line() +
  guides(colour=guide_legend(title=NULL), linetype = FALSE) +
 guides(linetype=guide_legend(title=NULL)) +
  theme_bw() +
  ggtitle("Health") + 
  ylab("Total number of references per UNGD session") + xlab("Year") +
  theme(legend.position="bottom") +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

  ggsave("PH_total_by_country_group.pdf")

Saving 7.29 x 4.51 in image

ggplot(proportions_groups, aes(x=Year, y=climate_speeches, group=country_groups, linetype = country_groups, colour = country_groups)) +
    geom_line() +
  guides(colour=guide_legend(title=NULL), linetype = FALSE) +
 guides(linetype=guide_legend(title=NULL)) +
  theme_bw() +
  ggtitle("Climate Change") + 
  ylab("Total number of references per UNGD session") + xlab("Year") +
  theme(legend.position="bottom") +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

  ggsave("CC_total_by_country_group.pdf")

Saving 7.29 x 4.51 in image

ggplot(proportions_groups, aes(x=Year, y=intersection_speeches, group=country_groups, linetype = country_groups, colour = country_groups)) +
    geom_line() +
  guides(colour=guide_legend(title=NULL), linetype = FALSE) +
 guides(linetype=guide_legend(title=NULL)) +
  theme_bw() +
  ggtitle("Intersection") + 
  ylab("Total number of references per UNGD session") + xlab("Year") +
  theme(legend.position="bottom") +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

  ggsave("intersection_total_by_country_group.pdf")

Saving 7.29 x 4.51 in image

##Proportion plots by country group

ggplot(proportions_groups, aes(x=Year, y=percent_PH, group=country_groups, linetype = country_groups, colour = country_groups)) +
    geom_line() +
  guides(colour=guide_legend(title=NULL), linetype = FALSE) +
 guides(linetype=guide_legend(title=NULL)) +
  theme_bw() +
  ggtitle("Discussion of Health") + 
  ylab("Proportion of Countries, %") + xlab("Year") +
  theme(legend.position="bottom") +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000,  2010, 2019))

  ggsave("PH_percent_country_group.pdf")

Saving 7.29 x 4.51 in image

ggplot(proportions_groups, aes(x=Year, y=percent_CC, group=country_groups, linetype = country_groups, colour = country_groups)) +
    geom_line() +
  guides(colour=guide_legend(title=NULL), linetype = FALSE) +
 guides(linetype=guide_legend(title=NULL)) +
  theme_bw() +
  ggtitle("Discussion of Climate Change") + 
  ylab("Proportion of Countries, %") + xlab("Year") +
  theme(legend.position="bottom") +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

  ggsave("CC_percent_country_group.pdf")

Saving 7.29 x 4.51 in image

ggplot(proportions_groups, aes(x=Year, y=percent_int, group=country_groups, linetype = country_groups, colour = country_groups)) +
    geom_line() +
  guides(colour=guide_legend(title=NULL), linetype = FALSE) +
 guides(linetype=guide_legend(title=NULL)) +
  theme_bw() +
  ggtitle("Discussion of intersection") + 
  ylab("Proportion of Countries, %") + xlab("Year") +
  theme(legend.position="bottom") +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

  ggsave("intersection_percent_country_group.pdf")

Saving 7.29 x 4.51 in image

#WHO regions

Total counts by WHO region

Using UN M49 Standard (Standard country or area codes for statistical use (M49)) we create regional and sub-regional groupings of countries. [https://unstats.un.org/unsd/methodology/m49/]

#loading WHO regions country identifiers and preprocessing
regions <- readr::read_csv("../UNSD - Methodology.csv")

Parsed with column specification:
cols(
  `Global Code` = col_double(),
  `Global Name` = col_character(),
  `Region Code` = col_double(),
  WHO = col_character(),
  `Region Name` = col_character(),
  `Sub-region Code` = col_double(),
  `Sub-region Name` = col_character(),
  `Intermediate Region Code` = col_double(),
  `Intermediate Region Name` = col_character(),
  `Country or Area` = col_character(),
  `M49 Code` = col_double(),
  `ISO-alpha3 Code` = col_character(),
  `Least Developed Countries (LDC)` = col_character(),
  `Land Locked Developing Countries (LLDC)` = col_character(),
  `Small Island Developing States (SIDS)` = col_character(),
  `Developed / Developing Countries` = col_character()
)

#UNGDC used YUG designation while WHO is using SRB
total_counts$country <- str_replace(total_counts$country, "YUG", "SRB")

#merging WHO designations with mention counts
regional_data <- inner_join(total_counts, regions, by = c("country" = "ISO-alpha3 Code"))

# total sum of mention counts by year and WHO region, with means
who_sum <- summarise(group_by(regional_data, year, WHO), 
                     who_int = sum(intersection_count), who_PH = sum(health_count), who_CC = sum(climate_count))

readr::write_csv(regional_data, "who_regions_data.csv")
readr::write_csv(who_sum, "figure58.csv")

Plot by region

ggplot(who_sum, aes(x=year, y=who_PH, group=WHO, linetype = WHO, colour = WHO)) +
    geom_line() +
  guides(colour=guide_legend(title=NULL), linetype = FALSE) +
 guides(linetype=guide_legend(title=NULL)) +
  theme_bw() +
  ggtitle("Health") + 
  ylab("Total number of references per UNGD session") + xlab("Year") +
  theme(legend.position="bottom") +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

  ggsave("PH_total_by_who.pdf")

Saving 7.29 x 4.51 in image

ggplot(who_sum, aes(x=year, y=who_CC, group=WHO, linetype = WHO, colour = WHO)) +
    geom_line() +
  guides(colour=guide_legend(title=NULL), linetype = FALSE) +
 guides(linetype=guide_legend(title=NULL)) +
  theme_bw() +
  ggtitle("Climate Change") + 
  ylab("Total number of references per UNGD session") + xlab("Year") +
  theme(legend.position="bottom") +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

  ggsave("CC_total_by_who.pdf")

Saving 7.29 x 4.51 in image

ggplot(who_sum, aes(x=year, y=who_int, group=WHO, linetype = WHO, colour = WHO)) +
    geom_line() +
  guides(colour=guide_legend(title=NULL), linetype = FALSE) +
 guides(linetype=guide_legend(title=NULL)) +
  theme_bw() +
  ggtitle("Intersection") + 
  ylab("Total number of references per UNGD session") + xlab("Year") +
  theme(legend.position="bottom") +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

  ggsave("intersection_total_by_who.pdf")

Saving 7.29 x 4.51 in image

Proportion by WHO region

Data preparation

climate_speeches_who <- regional_data %>% filter(climate_count>0) %>% group_by(year, WHO) %>% tally(name = "climate_speeches")
health_speeches_who <- regional_data %>% filter(health_count>0) %>% group_by(year, WHO) %>% tally(name = "health_speeches")
intersection_speeches_who <- regional_data %>% filter(intersection_count>0) %>% group_by(year, WHO) %>% tally(name = "intersection_speeches")

#merging WHO designations with mention counts
ungd_files$Country <- str_replace(ungd_files$Country, "YUG", "SRB")

total_speeches_who <- inner_join(ungd_files, regions, by = c("Country" = "ISO-alpha3 Code")) %>% 
  group_by(Year, WHO) %>% tally(name = "total_speeches")

proportions_who <- left_join(total_speeches_who, health_speeches_who, by = c("Year"="year", "WHO")) %>% 
  left_join(., climate_speeches_who, by = c("Year"="year", "WHO")) %>% 
  left_join(., intersection_speeches_who, by = c("Year"="year", "WHO")) %>% 
  replace_na(list(health_speeches=0, climate_speeches=0, intersection_speeches=0))

proportions_who$percent_int <- proportions_who$intersection_speeches/proportions_who$total_speeches*100
proportions_who$percent_CC <- proportions_who$climate_speeches/proportions_who$total_speeches*100
proportions_who$percent_PH <- proportions_who$health_speeches/proportions_who$total_speeches*100

readr::write_csv(proportions_who, "figure56.csv")

Plotting

ggplot(proportions_who, aes(x=Year, y=percent_PH, group=WHO, linetype = WHO, colour = WHO)) +
    geom_line() +
  guides(colour=guide_legend(title=NULL), linetype = FALSE) +
 guides(linetype=guide_legend(title=NULL)) +
  theme_bw() +
  ggtitle("Discussion of Health") + 
  ylab("Proportion of Countries, %") + xlab("Year") +
  theme(legend.position="bottom") +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

  ggsave("PH_percent_who.pdf")

Saving 7.29 x 4.51 in image

ggplot(proportions_who, aes(x=Year, y=percent_CC, group=WHO, linetype = WHO, colour = WHO)) +
    geom_line() +
  guides(colour=guide_legend(title=NULL), linetype = FALSE) +
 guides(linetype=guide_legend(title=NULL)) +
  theme_bw() +
 ggtitle("Discussion of Climate Change") + 
  ylab("Proportion of Countries, %") + xlab("Year") +
  theme(legend.position="bottom") +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

  ggsave("CC_percent_who.pdf")

Saving 7.29 x 4.51 in image

ggplot(proportions_who, aes(x=Year, y=percent_int, group=WHO, linetype = WHO, colour = WHO)) +
    geom_line() +
  guides(colour=guide_legend(title=NULL), linetype = FALSE) +
 guides(linetype=guide_legend(title=NULL)) +
  theme_bw() +
  ggtitle("Discussion of Intersection") + 
  ylab("Proportion of countries, %") + xlab("Year") +
  theme(legend.position="bottom") +
  scale_x_continuous(limits=c(1970, 2019), breaks = c(1970, 1980, 1990, 2000, 2010, 2019))

  ggsave("intersection_percent_who.pdf")

Saving 7.29 x 4.51 in image